System and method for improved signal detection in nmr spectroscopy

ABSTRACT

Systems and methods for improved NMR signal detection are described. The system receives NMR signal data produced by a sample over time in response to an excitation pulse and selects a predefined system function for application to the NMR signal data for systematic variation of signal properties The system function has a different influence on NMR signal components than on noise components of the sampled signal and has a variation parameter to control the systematic variation. A plurality of variation parameter values is provided with differing values to influence broad NMR signals as well as weak NMR signals. The system generates for each variation parameter value a corresponding intermediate data set by applying the system function with the respective variation parameter value to the NMR signal data. Further, from each intermediate data set, a respective base value centered spectrum is generated in the frequency-domain.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority based on EuropeanApplication No. EP18179985.9, filed on Jun. 26, 2018 and entitled“SYSTEM AND METHOD FOR IMPROVED SIGNAL DETECTION IN NMR SPECTROSCOPY”the entirety of which is hereby incorporated by reference.

TECHNICAL FIELD

The subject matter described herein generally relates to signaldetection in NMR spectroscopy and more particularly to improved signaldetection based on systematic variation of signal properties.

BACKGROUND

Nuclear magnetic resonance (NMR) spectroscopy is a spectroscopictechnique to observe local magnetic fields around atomic nuclei. Asample is placed in a magnetic field and the NMR signal is produced byexcitation of the nuclei sample with radio frequency (RF) pulses intonuclear magnetic resonance, which is detected with sensitive RFreceivers. The intramolecular magnetic field around an atom in amolecule changes the resonance frequency, thus giving access to detailsof the electronic structure of a molecule and its individual functionalgroups. For example, NMR spectroscopy is used to identify monomolecularorganic compounds, proteins and other complex molecules. Besidesidentification, NMR spectroscopy provides detailed information about thestructure, dynamics, reaction state, and chemical environment ofmolecules. Common types of NMR include proton and carbon-13 NMRspectroscopy, just to name a few examples.

Upon excitation of the sample with a radio frequency (typically 60-1000MHz) pulse, a nuclear magnetic resonance response is obtained which isreferred to as free induction decay (FID) herein. The FID is typically aweak signal and may require sensitive RF receivers to detect suchsignals. A Fourier transform can be applied to extract thefrequency-domain spectrum from the raw time-domain FID. A spectrum froma single FID typically has a low signal-to-noise ratio. Decay times ofthe excitation, typically measured in seconds, depend on theeffectiveness of relaxation, which is faster for lighter nuclei and insolids, and slower for heavier nuclei and in solutions whereas they canbe very long in gases.

Some existing NMR signal detection methods are based on computing thederivation of the frequency spectrum in a first step. As a consequence,broad signals (i.e., signals extending over a relatively large frequencyinterval in the frequency domain) get lost, whereas narrow signals arepreserved. Further the derivation generates artefacts which can lead tofalse positives and false negatives. Such disadvantages can be partiallyremedied with the Continuous Wavelet Transformation, for example.

SUMMARY

There is therefore a need to provide systems and methods for robustsignal detection where all relevant signals in a NMR signal data set canbe detected with a high probability, independent of their signalintensity and signal width (i.e., the width of respective signal peaks).

Embodiments of the claimed subject matter in the form of acomputer-implemented method, computer system and computer programproduct solve this technical problem using the claimed features.

In one embodiment, a computer-implemented spectroscopic method forimproved NMR signal detection is provided. The computer-implementedmethod can be executed by a computer system which can process arespective computer program product. The computer system has aninterface to receive NMR signal data produced by a sample over time inresponse to an excitation pulse. Typically, the excitation pulse is anRF-pulse and the response of the sample, the free induction decay FID,is an exponentially falling, weak radio signal which typically ceaseswithin about two seconds after the excitation pulse. When transformingthis signal from the time-domain into the frequency domain (Fouriertransformation), the resulting frequency spectrum shows multiple signalpeaks at different frequencies which are indicative of certain nuclearcores (e.g., ¹H, ¹³C, ¹⁵N, etc.). The amplitude of such peaks can besmall and difficult to distinguish from noise components of the signal.At the same time the width of some peaks can be quite broad (extendingover a relatively large frequency range).

To better distinguish real NMR signal components from noise components,the claimed approach uses a predefined system function and applies thesystem function to the NMR signal data for systematic variation ofsignal properties. Thereby, the NMR signal data may be the originallysampled data in the time-domain or the data may already be transformedinto the frequency-domain. The system function can be selected from agroup of possible system functions suitable for the respective domain.For example, there are system functions which are applicable to the rawtime-domain data (the data directly obtained from the measurement)whereas other system functions can be applied to the correspondingfrequency spectrum. Appropriate system functions include but are notlimited to: convolution in the Frequency domain using a Lorentzfunction, Gaussian function or Trapezium function, and multiplication inthe time domain using an exponential decay function, exponential slopefunction or trigonometric function. For example, using multiplication ofNMR signal data in the time domain with an exponential decay function isadvantageous with regards to the computational efficiency of the system.In the time domain, the noise components dominate at a later delay thanthe signal components. So multiplication of the raw data in the timedomain with an exponential decay function results in a higheramplification of the signal components than of the noise components.

For the claimed approach, the system function may be applied in thetime-domain or in the frequency-domain as long as an appropriate systemfunction is selected for the respective domain. The selected systemfunction is adapted to have a different influence on NMR signalcomponents than on noise components of the sampled signal. Further, thesystem function has a variation parameter which allows to control thesystematic variation of the signal properties. In other words, applyingthe system function with multiple different variation parameter valuesto the NMR signal data (either in the time of frequency domain) allowsto produce multiple variation versions of the NMR signal data. In otherwords, when applying the system function with the respective variationparameter values to the sampled NMR signal data a correspondingintermediate data set is generated for each variation parameter value.In practice, about 3 to about 20 variation parameters can be used.Advantageously, 5 to 10 variations parameter values may be used.

It can be advantageous to use a variation parameter with Hertz as theunit of measurement. The variation parameter values may be provided aspredefined parameters which are of the order of the half-width of signalpeaks in the frequency-domain of the received NMR data set. That is, theparameter values are defined in such a way that each relevant peak ofthe spectrum (in the frequency-domain) is influenced by at least one ofthe variation parameter values. The variation parameter values may bepredefined based on the experience gained from previous measurements.Advantageously, the differing values of the variation parameter valuescan be selected to influence, in the frequency-domain, NMR signals witha signal intensity within a given interval around the mean signalintensity of the sampled signal, and NMR signals with a signal widthwithin a given interval around the mean signal width. Such variationparameter values ensure that sharp signals with low signal intensity aswell as broad signals will be substantially affected by the applicationof the system function which will finally allow to detect such signalswith high certainty. Affecting a signal substantially when applying thesystem function means that the different variation parameter values leadto a significant variation (e.g., variance) of the processed signalvalues in the respective intermediate data sets. In one embodiment, thesystem can automatically analyze the peaks in the frequency spectrum ofthe received NMR data set. The system can then automatically determineappropriate parameter values based on the height and width of theobserved peaks in accordance with the above selection criteria.

It is to be noted that certain system functions are adapted to beapplied in the time-domain, whereas other system functions are adaptedto be applied in the frequency domain. With the implementation ofcertain system functions, such as, for example, the exponential decayfunction, the application of the system function in the time-domainleads to a computationally efficient embodiment.

The computer system then generates from each intermediate data set arespective base value centered spectrum in the frequency-domain. In casethe intermediate sets were generated in the time-domain a Fouriertransformation can be used to transform the intermediated data sets intothe frequency domain. In case the intermediate data sets were alreadygenerated in the frequency-domain no such transformation is necessary atthis stage. For generating the base value centered spectra the computersystem computes for each frequency point of the intermediate data setsin the frequency-domain an ensemble base value based on the respectivevalues of all intermediate data sets. That is, the ensemble base valueat a particular frequency point may be formed, for example, as thearithmetic mean of the respective intermediate data set values at theparticular frequency point. Other mean values or statistical methods maybe used to compute an appropriate base value, including but not limitedto geometric mean, harmonic mean, quadratic mean, median value, etc. Foreach frequency the respective computed ensemble base value is thensubtracted from the respective values of the intermediate data setsresulting in the corresponding base value centered spectrum for eachintermediate data set. That is, if N variation parameter values wereused for the system function N base value centered spectra will resultfrom this step. In other words, the base value centered spectraeliminate offsets from the intermediate data sets. This includesapproximating corresponding base values representing the actual offsets.

In the following steps, the system distinguishes the NMR signalcomponents from the noise components which are included in the receivedraw signal data. This is achieved by extracting from the base valuecentered spectra for each frequency point variations induced by thesystem function and identifying frequency intervals with significantvariation as signal intervals.

In one embodiment, the system generates a (single) deviation spectrum(e.g., a standard deviation spectrum) from the plurality of generatedbase value centered spectra. The deviation spectrum includes frequencyintervals which include peaks resulting from variations induced by thesystem function, and further includes frequency intervals with noisecomponents only. For determining a noise value, the system selects anon-signal (frequency) interval in the deviation spectrum. A non-signalinterval can easily be determined by looking at a particular frequencyinterval which does not show any peaks and analyzing whether the signalin the particular frequency interval shows a normal distribution andtherefore qualifies as a mere noise signal with no NMR signal componentsincluded. A weighted noise value may then be determined for non-signalintervals in the deviation spectrum.

In one embodiment, a noise value can then be determined by computing themean value and the standard deviation for the selected non-signalfrequency interval in the deviation spectrum. The computed standarddeviation can then be multiplied with a predefined weighting factorresulting in a weighted noise value. The weighting factor is selected sothat a threshold probability is determined for values in the deviationspectrum which ensures that values being lower than or equal to theweighted noise value to qualify as noise values with said thresholdprobability. For example, multiplying the computed standard deviationwith a weighting factor of 3.5 implies a 99 percent probability that allvalues beneath the weighted noise value are mere noise components of thesignal and do not include any NMR signal components.

In an alternative embodiment, iterative thresholding may be used todetermine the weighted noise values. Iterative thresholding algorithmsare well-known by the skilled person as technique for determining noisevalues.

Frequency intervals with significant variations are then identified assignal intervals.

In the embodiment using the deviation spectrum, this is achieved bydetermining values in the deviation spectrum which are higher than theweighted noise value as NMR signal components. In other words, thoseparts of the base value centered spectra significant variations inducedby the system function for different variation parameter values indicatefrequency intervals with NMR signal components. They can be easilyextracted as the peaks exceeding the weighted noise level in thedeviation spectrum. In other words, a significant variation is presentwhen a peak in the deviation spectrum exceeds the weighted noise level.

In an alternative embodiment for detecting the signal intervals, insteadof using the deviation spectrum, the system generates an eigenspacematrix from the plurality of generated base value centered spectra toextract the induced variations. For example, the eigenspace matrix caninclude a row for each base value centered spectrum where each columnincludes the spectrum values for the respective frequency points. Therelevant eigenvalues are determined by a threshold. For example, arelative threshold of a value of 10⁻⁵ may be used, i.e., all eigenvaluesabove the maximum eigenvalue multiplied with the threshold are used.Then, the absolute values of the product of the first m (with m=1; 2; 3;. . . ) eigenvectors and eigenvalues of the matrix are used. Theabsolute values represent the variations induced by the system function.If more than one eigenvector result is used, the resulting eigenvectorsare summed up. In some cases, the absolute values of the firsteigenvector of the matrix may already be sufficient and no summation isrequired. The eigenvector result is similar to the deviation spectrumwhere the absolute values of the at least first eigenvector (or therespective sum of eigenvectors) represent the system function inducedvariations.

Based on the eigenvector result, weighted noise values can be determinedby using the same methodologies as disclosed in the deviation spectrumembodiment. The absolute values of the eigenvector result are thencompared with the respective weighted noise values and NMR signalcomponents are identified for such frequency points where the absolutevalues are greater than the weighted noise values.

In further embodiments, a computer program product when loaded into amemory of a computer system and executed by at least one processor ofthe computer system causes the computer system to execute the steps ofthe herein disclosed computer implemented method for functions of thecomputer system as disclosed herein.

The computer system for improved NMR signal detection in NMRspectroscopy can be summarized as a system having an interface module toreceive NMR signal data produced by a sample over time in response to anexcitation pulse.

An intermediate data set generator of the system selects a predefinedsystem function for application to the sampled NMR signal data forsystematic variation of signal properties. The system function has adifferent influence on NMR signal components than on noise components ofthe sampled signal. Further, the system function has a variationparameter to control the systematic variation. Thereby, differentvariation parameter values result in different variations when applyingthe system function to sampled NMR signal data.

A plurality of variation parameter values is provided to the system(either predefined or determined by the system) wherein the selectedparameter values have differing values which are adapted for influencingthe sampled NMR signals in the frequency-domain. Thereby, the influenceextends to NMR signals with a signal intensity within a given intervalaround the mean signal intensity of the sampled signal, and NMR signalswith a signal width within a given interval around the mean signalwidth. In other words, the variation parameter values are selected in away that the system function affects every signal component in thesampled NMR signal data in at least one variation parameter setting.

The intermediate data set generator (IDSG) then generates for eachvariation parameter value a corresponding intermediate data set byapplying the system function with the respective variation parametervalue to the sampled NMR signal data.

The system further has a base value centered spectrum (BVCS) generatorto generate from each intermediate data set, in the frequency-domain, arespective base value centered spectrum. In case the intermediated datasets are in the time-domain, the BVCS generator may include atime-domain-to-frequency-domain transformer. This may be implemented asa Fourier-Transformation to convert time-domain data intofrequency-domain data before BVCS generation. The resulting base valuedspectra have reduced offsets and include peaks and noise around a zerobase line.

The system further has a signal detector to generate a deviationspectrum from the plurality of generated base value centered spectra.The deviation spectrum shows significant variations for frequency pointswhere NMR signal components were affected by the system function. Forfrequency points without NMR signal components only the noise componentsare present. In such non-signal (frequency) intervals in the deviationspectrum the signal detector determines a weighted noise value for thedeviation spectrum. Any peak of the deviation spectrum exceeding theweighted signal noise value corresponds to a sampled NMR signalcomponent with a probability associated with the weighted noise value.

A signal-noise comparator of the signal detector detects signalintervals by extracting from the base value centered spectra for eachfrequency point variations induced by the system function. The frequencyintervals with significant variations are identified identify as signalintervals. As discussed above, the signal-noise comparator may work onthe basis of the deviation spectrum or it may use the eigenvectordetermination method as disclosed herein.

Further aspects of the claimed subject matter will be realized andattained by means of the elements and combinations particularly depictedin the appended claims. It is to be understood that both, the foregoinggeneral description and the following detailed description are exemplaryand explanatory only and are not restrictive of the invention asdescribed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computer system for improved NMR signaldetection in NMR spectroscopy according to an embodiment.

FIG. 2 is a simplified flow chart of a computer-implementedspectroscopic method for improved NMR signal detection according to anembodiment.

FIGS. 3A, 3B show examples of NMR signal data in the time- and frequencydomains.

FIG. 4A illustrates an example of a system function in the time-domain.

FIG. 4B illustrates an example of a system function in thefrequency-domain.

FIGS. 5A, 5B, 5C show intermediate data sets as result of theapplication of a system function to NMR signal data with different VPvalues.

FIGS. 6A, 6B, 6C, 6D show base value centered spectra computed based onthe intermediate data sets.

FIGS. 7A and 7B relate to an embodiment using a deviation spectrum forextracting variations induced by a system function from base valuecentered spectra.

FIGS. 8A, 8B shows signal interval graphs with original NMR signal dataand an overlay curve indicating signal intervals according to anembodiment.

FIG. 9 is a diagram that shows an example of a generic computer deviceand a generic mobile computer device which may be used with thetechniques described herein.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a computer system 100 for improved NMRsignal detection in NMR spectroscopy according to an embodiment. Thesystem 100 of FIG. 1 is described in the context of the simplified flowchart of a computer-implemented spectroscopic method 1000 for improvedNMR signal detection as illustrated in FIG. 2. Therefore, the followingdescription refers to reference numbers used in FIG. 1 and FIG. 2. Thesystem 100 is thereby configured to execute the method 1000 when loadinga respective computer program into a memory of the system and executingsaid program with processing means of the system.

The computer system 100 includes an interface module 110 forcommunicative coupling of the system 100 to a NMR system 200 forperforming NMR measurements on a sample 201. Further, the interface 110may be coupled with an Input/Output (I/O) unit 300 which allows a humanuser to interact with the computer system 100. Via the interface 110,the system receives 1100 NMR signal data 202 produced by the sample 201over time in response to an excitation pulse. Such measurementstechniques are well known in the art. The received signal data 202 maybe in the time-domain or in the frequency-domain dependent on the datapre-processing functions of the NMR system 200. The original raw datacaptured by the NMR system 200 are in the time-domain measuring thesignal decay over time. However, appropriate data pre-processing meansof the NMR system 200 may already provide a frequency spectrum based onthe captured data. It is to be noted that, form a conceptualperspective, it is irrelevant whether the received signal data 202 is inthe time- or frequency-domain as data in each domain can be transformedinto data of the other domain without any information loss.

The received NMR signal data 202 forms input to an intermediate data setgenerator (IDSG) 120. The IDSG 120 can access one or more predefinedsystem functions 121. The system function(s) may be stored by thecomputer system or can be at least accessed by the system on a remotestorage. A particular predefined system function 121 can be applied tothe sampled NMR signal data 202 for systematic variation of signalproperties. Thereby, the system function 121 has a different influenceon NMR signal components than on noise components of the sampled signal.Further, the system function has a variation parameter (VP) 122 tocontrol the systematic variation. In other words, a system function(when applied to the received NMR signal data 202 using multiple varyingvariation parameters) will affect the original signal data which includeonly noise components in a different way than it affects parts of thesignal data which include NMR signal components.

The IDSG 120 selects 1200 a system function which is appropriate for thedomain in which the NMR signal data 202 is received. If the receiveddata 202 is in the time-domain, the system function can beadvantageously selected from one or more of the following group offunctions: exponential decay function, exponential slope function, or atrigonometric function (e.g., sinus or cosine function). However, othersystem functions may also be used which are appropriate for signal datain the time-domain. This signal function is applied to the receivedsignal data by multiplying the system function with the NMR signal data202. If the received signal data is in the frequency domain the systemfunction can be advantageously selected from the following group offunctions: Lorentz function, Gaussian function, or Trapezium Function.The system function is then used in a convolution with the NMR signaldata 202.

For the variation of the system function a VP provisioning module 122provides a plurality of variation parameter values. The parameter valuesselected for the variation have differing values covering an appropriatevalue range. The selected VP values are adapted for influencing thesignal data 202 when represented in the frequency-domain so that NMRsignals are affected which have a signal intensity within a giveninterval around the mean signal intensity of the sampled signal data202, and which have signal width within a given interval around the meansignal width. That is, the VP values are selected 1300 to ensure thatnarrow signals with lower intensity may similarly be affected as broadsignals with higher intensity. This is achieved by using a range of VPvalues so that each NMR signal component is affected by at least one ofthe selected VP values when applying the system function accordingly.The VP values may be automatically determined by the computer systembased on such criteria or they may be provided by user of the computersystem via the I/O unit 300. Advantageously, the variation parameter ofthe system function has Hertz as the unit of measurement and the numberof variation parameter values is in the range from 3 to 20. Preferably,the number of VP values is in the range from 5 to 10.

The IDSG 120 then generates 1400 for each selected variation parametervalue a corresponding intermediate data set 102-1 to 102-n by applyingthe system function 121 with the respective variation parameter value tothe sampled NMR signal data 202. If the number of selected VP values isn this results inn intermediate data sets 102-1 to 102-n. Eachintermediate data set shows different values for such sampled datapoints where NMR signal components are present.

A base value centered spectrum (BVCS) generator 130 of the computersystem 100 generates 1500 from each intermediate data set 102-1 to102-n, in the frequency-domain, a respective mean base value centeredspectrum 103-1 to 103-n. As this step is performed in the frequencydomain, a transformation of the intermediate data sets into thefrequency-domain occurs in case the system function was applied in thetime-domain, for example by using Fourier Transformation. The respectivebase value centered spectra eliminate offsets from the intermediate datasets. This is achieved by approximating corresponding base valuesrepresenting the actual offsets and deducting the approximated offsetsfrom the intermediate data set values.

For generating the base value centered spectra the BVCS generator 130computes for each frequency point of the intermediate data sets in thefrequency-domain an ensemble base value based on the respective valuesof all intermediate data sets. That is, the ensemble base value at aparticular frequency point may be formed, for example, as the arithmeticmean of the respective intermediate data sets values at the particularfrequency point. Other mean values or statistical methods may be used tocompute an appropriate base value, including but not limited togeometric mean, harmonic mean, quadratic mean, median value, etc. Foreach frequency the respective computed ensemble base value is thensubtracted from the respective values of the intermediate data setsresulting in the corresponding base value centered spectrum for eachintermediate data set. If n variation parameter values were used forsignal variation through the system function n base value centeredspectra result from this step.

The generated base value centered spectra serve as input to a signaldetector 140 of the computer system 100. The signal detector 140 detects1600 signal intervals 109 by extracting from the base value centeredspectra 103-1 to 103-n for each frequency point variations induced bythe system function. The detector 140 finally identifies frequencyintervals showing significant variations as signal intervals. The signaldetector may be implemented by various embodiments.

In a first embodiment, the signal detector 140 extracts the variationsfrom the base value centered spectra 103-1 to 103-n by firstlygenerating a deviation spectrum from the plurality of generated basevalue centered spectra 103-1 to 103-n. That is, for each frequency pointthe standard deviation is computed taking into account the respectivevalues of all generated base value centered spectra.

Then, a noise value is determined. The noise signal corresponds to anormal distribution and can be determined by computing the mean valueand the standard deviation for a non-signal interval in the deviationspectrum. The non-signal interval may be any frequency interval in thedeviation spectrum corresponding to a frequency interval having a normaldistribution. The system may first select an interval where no signalpeaks appear, and may then analyze whether the selected intervalcomplies with the normal distribution criterion. If so, the noise valueis computed. If not, another interval is selected and the same tests areperformed until a mere noise interval (i.e., an interval without NMRsignal components) is finally determined.

The computed noise value may then be multiplied with a predefinedweighting factor. The weighting factor determines a thresholdprobability for values in the deviation spectrum which are lower than orequal to the weighted noise value to qualify as noise values with saidthreshold probability. For example, when using the standard deviationmultiplied with a factor of 3.5 to compute the weighted noise valuethere is a 99 percent probability that all values below the weightednoise value actually represent noise components of the signal. Thevalues in the deviation spectrum which are higher than the weightednoise value qualify as NMR signal components. Once the signal componentsare identified the respective signal intervals correspond to thefrequency intervals in the deviation spectrum including the detected NMRsignal components.

Optionally, the computer system may include a smoothening and phasecorrection module 150 to smoothen the basis line in the deviationspectrum and to perform a phase correction on the detected NMR signalintervals.

In another embodiment, the signal detector 140 generates an eigenspacematrix from the plurality of generated base value centered spectra 103-1to 103-n and determines the absolute values of at least the firsteigenvector of the matrix. Such absolute values represent the systemfunction induced variations. The eigenspace matrix can be constructed byincluding a row for each base value centered spectrum where each columnincludes the spectrum values for the respective frequency points. Therelevant eigenvalues are determined by a threshold. For example, arelative threshold of a value of 10⁻⁵ may be used, e.g., all eigenvaluesabove maximum eigenvalue times the relative threshold are used. Then theabsolute values of the corresponding eigenvectors, multiplied with theireigenvalues are used. If more than one eigenvector result is used, theresulting eigenvectors are summed up. In some cases, the absolute valuesof the first eigenvector of the matrix may already be sufficient and nosummation is required. The eigenvector result is similar to thedeviation spectrum where the absolute values of the at least firsteigenvector (or the respective sum of eigenvectors) represent the systemfunction induced variations.

Based on the eigenvector result, weighted noise values can be determinedby using the same methodologies as disclosed in the deviation spectrumembodiment. The absolute values of the eigenvector result are thencompared with the respective weighted noise values and NMR signalcomponents are identified for such frequency points where the absolutevalues are greater than the weighted noise values.

In a further alternative embodiment, the signal detector 140 usesiterative thresholding when extracting the variation for each frequencypoint from the base value centered spectra 103-1 to 103-n to detect theNMR signal intervals. An initial threshold value is determined bycomputing the standard deviation of the respective spectrum andmultiplying the standard deviation with a predefined factor. Thepredefined factor is called noise factor. Again, the basic assumptionhere is that noise follows a Gaussian normal distribution. The noisefactor represents the confidence interval. In a next step a new standarddeviation is determined based on all points of the spectrum which aresmaller than the initial threshold. A new threshold is then computedbased on the new standard deviation and the noise factor. This processis then iterated until the new standard deviation differs from thepreviously calculated standard deviation in less than a predefined deltavalue. Based on the last determined standard deviation the remainingsteps of the first signal detector embodiment can be applied to finallydetect the signal intervals.

In the following, additional embodiments are described based on thefirst signal detector embodiment. However, a person skilled in the artcan easily transfer the disclosed teachings to also implementalternative single detector embodiments.

FIG. 3A illustrates received NMR signal data 311 in the time-domain. Thetime scale 310-x of the signal graph 310 is in multiples of Dwell time(in this example 14 microseconds). The signal intensity is given inarbitrary units of the measurement. In NMR, the Dwell Time is defined asthe number of seconds between points during data acquisition in the FID,which is the same as seconds/point. The figure shows a quick signaldecay which occurs after the excitation of the sample with theexcitation pulse. FIG. 3B shows the real part 321 of the NMR signal data311 when transformed into a frequency spectrum 320 after a FourierTransformation of the time signal 311. The imaginary part of thefrequency spectrum is not shown here but would also be needed totransform the NMR signal data in the frequency-domain back into the timedomain using inverse Fourier Transformation. Both domain representationsof the received NMR signal data may be used as an input for the IDSgenerator.

The IDS generator then applies a predefined system function to thereceived NMR signal data in accordance with the respective domain.Examples of appropriate system functions were discussed already above.In the following example, the system function ƒ(t)=e^(−LB*π*DW*t) isused. Such an exponential function has proven to be computationallyefficient for application in the time-domain. In the example systemfunction f(t), DW corresponds to the Dwell time (the distance betweentwo data points in the time-domain). The parameter LB corresponds to thevariation parameter of the system function and has Hz as the unit ofmeasurement with the advantage that it can easily be adjusted fordifferent Larmor frequencies (e.g., ¹H, ¹³C, ¹⁵N). Different VPparameter values affect the signal width (in the frequency-domain) sothat multiplying the received NMR signal data with the parameterizedsystem functions leads to different intermediate data sets where eachintermediate data set associated with a particular VP value showsdifferent peak width and peak height values for a respective signal peakof the original NMRS signal data. Advantageously, the variation of thevariation parameter values is in a range which covers the peak widths ofall NMR signal peaks in the frequency-domain. FIG. 4A illustrates thesystem function f(t) in the time-domain 410 (time axis 410-x) for threedifferent VP values (in the example: 1 Hz, 10 Hz, and 20 Hz) resultingin three instances 411, 412, 413 of the system function f(t). In thisfigure and in the following spectrum figures, the unit of the x-axis ofa graph nnn is referred to as nnn-x and the unit of the y-axis of saidgraph is referred to as nnn-y.

FIG. 4B illustrates an example of a system function 420 in thefrequency-domain (x-axis 420-x in Hz) which can be used for aconvolution with received NMR signal data in the frequency domain.Again, three different VP values lead to three different instances 421,422, 423 of the system function.

FIG. 5A shows three intermediate data sets 501, 502, 503 as the resultof the application of said system function to the NMR signal data withthree different VP values. The example graph 510 shows the intermediatedata sets in the frequency-domain with the unit 510-x (Points, distancebetween points is dwell time DW). The intensity 510-y has arbitraryunits from the measurement. In the example, the influence of the appliedsystem function on the signal height is increasing from intermediatedata set 501 to intermediate data set 503 (i.e., the respective peakheights are increasing). This example shows how the application of thesystem functions with different VP values generates a plurality ofintermediate data sets with a substantial variation in such parts of theNMR signal spectrum which includes NMR signal components whereas suchparts including only noise components are hardly affected by thevariation.

FIG. 5B shows a more detailed view 520 of intermediate data sets 521 to523 focusing on only a small portion in the frequency dimension 520-xwhere only a single NMR signal peak was included. The shape of thesignal peak is substantially affected by the system function for threedifferent VP values leading to substantial differing variations of theoriginal NMR signal for differing VP values. FIG. 5C shows an overlaygraph 530 where the intermediate data sets of FIG. 5B are printed asoverlays at substantially the same y values in the 530-y dimension. Theoverlay graph clearly visualizes the variations of the respective signalpeak in the intermediate data sets 531 to 533. The position of the peakin the frequency dimension 530-x corresponds to the position in FIG. 5B.

The intermediate data sets (e.g., 511 to 513 of FIG. 5A) serve as inputto the BVCS generator for generating respective base value centeredspectra 611 to 613. The base value centered spectra in the BVCS graph610 of FIG. 6A show the result of processing the intermediate data setsfor eliminating offsets in such a way that the result leads to curveswhich are horizontally aligned around zero, removing the local basevalues. Thereby, the base values are an approximation of the actualoffsets of the respective curve. The BVCS spectra are always determinedin the frequency-domain. For example, the BVCS generator can compute theBVCS spectra by determining a mean value for each frequency point basedon all intermediated data sets. That is, in the example only 3 VP valueswere used which leads to three intermediate data sets. For eachfrequency point of the intermediate data sets the corresponding spectrumvalue is used to compute a mean value across all intermediate data sets.In a real signal detection scenario the number of intermediate data setscan be higher but advantageously does not exceed 20. The mean value canbe computed as average value (e.g., arithmetic average or otheraverages) or it can be computed by using appropriate statisticalmethods, such as for example, determining the median. The computed meanvalues for all frequency points are then subtracted from the respectivespectrum values of the intermediate data sets leading to the BVCS 611 to613. FIG. 6B shows another BVCS graph 620 with a more detailed view on afrequency interval of the spectra including a single peak located closeto the frequency 36650 620-x.

FIG. 6C shows the base value centered spectra 631 to 633 as an overlaygraph 630 corrected by the offsets approximated by the determined basevalues. As a result, all BVCS are now centered around zero in the 630-ydimension. FIG. 6D again shows a detailed overlay view 640 of afrequency range including a single peak for three BVCS 641, 642, 643associated with three different VP values.

FIGS. 7A and 7B relate to the embodiment using a deviation spectrum 710,720 for extracting variations induced by the system function from thebase value centered spectra for each frequency point. In FIG. 7A, thedeviation spectrum 710 is shown for the entire frequency range of therespective base value centered spectra. The peak 711-1 indicates a firstfrequency interval with a high variation (significant variation peak)which is an indicator for the presence on NMR signal components in thefirst frequency interval. A second frequency range at 711-n showssubstantially no variation at all which is an indicator that within thesecond frequency interval only noise components are present but no NMRsignal components.

FIG. 7B shows a detailed view of the deviation spectrum 720 over aportion of the frequency range with a variation double peak in frequencyinterval 712-2 and substantially no variation in frequency interval712-n. Based on the deviation spectrum 720 (or 710) the system candetermine a noise value. A noise signal follows a Gaussian normaldistribution. The system can select any of the frequency intervalsshowing substantially no variation (e.g., frequency intervals 711-n,712-n) and computing the mean value and the standard deviation for this(non-signal) interval in the deviation spectrum. If the standarddeviation follows a normal distribution a frequency interval isidentified which has no NMR signal components but only noise components.The computed noise value can then be multiplied with a predefinedweighting factor. The weighting factor determines a thresholdprobability for values in the deviation spectrum which are lower than orequal to the weighted noise value to qualify as noise values with saidthreshold probability. The noise factor can be chosen in the range ofthe half width of the Gaussian curve. Advantageously, the factor is 3.5covering 99 percent of the peak integral. In other words, multiplyingthe standard deviation with a factor of 3.5 can be used to achieve aprobability of 0.99 that all values of the deviation spectrum which havea value lower than then weighted noise value actually represent noise.

Once the weighted noise value is determined the values in the deviationspectrum which are higher than the weighted noise value are identifiedas NMR signal components. This allows to detect, in the deviationspectrum, the frequency intervals where NMR signal components arepresent. The detected NMR signal frequency intervals are then used inthe original NMR signal data (in the frequency-domain, or in thetime-domain after an inverse Fourier Transformation) to filter out thenoise parts of the spectrum. FIG. 8A shows a signal interval graph 810with the original NMR signal data 811 and an overlay curve 812 (dottedline) indicating the signal intervals. However, when looking at theentire frequency range of FIG. 8A the signal intervals cannot beresolved by the human eye in graph 810. Therefore, FIG. 8B again shows adetailed view 820 for a part of the frequency range including the foursignal intervals I1, I2, I3 and I4.

The identified signal intervals (e.g., I1, I2, I3 and I4) allow robustNMR signal detection with higher accuracy than prior art solutions forsignal detection. On the basis of the identified signals highly preciseNMR analysis becomes possible.

The embodiment illustrated in the detailed description focuses on thestandard deviation method. A person skilled in the art will acknowledgethat the previously described alternative embodiments based on iterativethresholding or based on the eigenvector method lead to similar robustand accurate detection of NMR signal intervals and therefore alsoprovide a robust technical solution for the technical problem to providean improved NMR signal detection method.

FIG. 9 is a diagram that shows an example of a generic computer device900 and a generic mobile computer device 950, which may be used with thetechniques described here. In some embodiments, computing device 900 mayrelate to the system 100 (cf. FIG. 1). Computing device 950 is intendedto represent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart phones, and other similarcomputing devices. In the context of this disclosure the computingdevice 950 may provide the I/O means of FIG. 1. In other embodiments,the entire system 100 may be implemented on the mobile device 950. Thecomponents shown here, their connections and relationships, and theirfunctions, are meant to be exemplary only, and are not meant to limitimplementations of the subject matter described and/or claimed in thisdocument.

Computing device 900 includes a processor 902, memory 904, a storagedevice 906, a high-speed interface 908 connecting to memory 904 andhigh-speed expansion ports 910, and a low speed interface 912 connectingto low speed bus 914 and storage device 906. Each of the components 902,904, 906, 908, 910, and 912, are interconnected using various busses,and may be mounted on a common motherboard or in other manners asappropriate. The processor 902 can process instructions for executionwithin the computing device 900, including instructions stored in thememory 904 or on the storage device 906 to display graphical informationfor a GUI on an external input/output device, such as display 916coupled to high speed interface 908. In other implementations, multipleprocessors and/or multiple buses may be used, as appropriate, along withmultiple memories and types of memory. Also, multiple computing devices900 may be connected, with each device providing portions of thenecessary operations (e.g., as a server bank, a group of blade servers,or a multi-processor system).

The memory 904 stores information within the computing device 900. Inone implementation, the memory 904 is a volatile memory unit or units.In another implementation, the memory 904 is a non-volatile memory unitor units. The memory 904 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 906 is capable of providing mass storage for thecomputing device 900. In one implementation, the storage device 906 maybe or contain a computer-readable medium, such as a floppy disk device,a hard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer- ormachine-readable medium, such as the memory 904, the storage device 906,or memory on processor 902.

The high speed controller 908 manages bandwidth-intensive operations forthe computing device 900, while the low speed controller 912 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In one implementation, the high-speed controller 908 iscoupled to memory 904, display 916 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 910, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 912 is coupled to storage device 906 and low-speed expansionport 914. The low-speed expansion port, which may include variouscommunication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)may be coupled to one or more input/output devices, such as a keyboard,a pointing device, a scanner, or a networking device such as a switch orrouter, e.g., through a network adapter.

The computing device 900 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 920, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 924. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 922. Alternatively, components from computing device 900 may becombined with other components in a mobile device (not shown), such asdevice 950. Each of such devices may contain one or more of computingdevice 900, 950, and an entire system may be made up of multiplecomputing devices 900, 950 communicating with each other.

Computing device 950 includes a processor 952, memory 964, aninput/output device such as a display 954, a communication interface966, and a transceiver 968, among other components. The device 950 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 950, 952,964, 954, 966, and 968, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 952 can execute instructions within the computing device950, including instructions stored in the memory 964. The processor maybe implemented as a chipset of chips that include separate and multipleanalog and digital processors. The processor may provide, for example,for coordination of the other components of the device 950, such ascontrol of user interfaces, applications run by device 950, and wirelesscommunication by device 950.

Processor 952 may communicate with a user through control interface 958and display interface 956 coupled to a display 954. The display 954 maybe, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display)or an OLED (Organic Light Emitting Diode) display, or other appropriatedisplay technology. The display interface 956 may comprise appropriatecircuitry for driving the display 954 to present graphical and otherinformation to a user. The control interface 958 may receive commandsfrom a user and convert them for submission to the processor 952. Inaddition, an external interface 962 may be provide in communication withprocessor 952, so as to enable near area communication of device 950with other devices. External interface 962 may provide, for example, forwired communication in some implementations, or for wirelesscommunication in other implementations, and multiple interfaces may alsobe used.

The memory 964 stores information within the computing device 950. Thememory 964 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory 984 may also be provided andconnected to device 950 through expansion interface 982, which mayinclude, for example, a SIMM (Single In Line Memory Module) cardinterface. Such expansion memory 984 may provide extra storage space fordevice 950, or may also store applications or other information fordevice 950. Specifically, expansion memory 984 may include instructionsto carry out or supplement the processes described above, and mayinclude secure information also. Thus, for example, expansion memory 984may act as a security module for device 950, and may be programmed withinstructions that permit secure use of device 950. In addition, secureapplications may be provided via the SIMM cards, along with additionalinformation, such as placing the identifying information on the SIMMcard in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 964, expansionmemory 984, or memory on processor 952, that may be received, forexample, over transceiver 968 or external interface 962.

Device 950 may communicate wirelessly through communication interface966, which may include digital signal processing circuitry wherenecessary. Communication interface 966 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 968. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 980 mayprovide additional navigation- and location-related wireless data todevice 950, which may be used as appropriate by applications running ondevice 950.

Device 950 may also communicate audibly using audio codec 960, which mayreceive spoken information from a user and convert it to usable digitalinformation. Audio codec 960 may likewise generate audible sound for auser, such as through a speaker, e.g., in a handset of device 950. Suchsound may include sound from voice telephone calls, may include recordedsound (e.g., voice messages, music files, etc.) and may also includesound generated by applications operating on device 950.

The computing device 950 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 980. It may also be implemented as part of a smartphone 982, personal digital assistant, or other similar mobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing device that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing device can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

What is claimed is:
 1. A computer-implemented spectroscopic method forimproved NMR signal detection, comprising: receiving NMR signal dataproduced by a sample over time in response to an excitation pulse;selecting a predefined system function for application to the NMR signaldata for systematic variation of signal properties wherein the systemfunction has a different influence on NMR signal components than onnoise components of the sampled signal, the system function having avariation parameter to control the systematic variation; providing aplurality of variation parameter values wherein the selected parametervalues have differing values which are adapted for influencing, in thefrequency-domain, NMR signals with a signal intensity within a giveninterval around the mean signal intensity of the sampled signal, and NMRsignals with a signal width within a given interval around the meansignal width; generating for each variation parameter value acorresponding intermediate data set by applying the system function withthe respective variation parameter value to the NMR signal data;generating from each intermediate data set a respective base valuecentered spectrum in the frequency-domain wherein the respective basevalue centered spectra eliminate offsets from the intermediate data setsincluding approximating corresponding base values representing theactual offsets, wherein generating base value centered spectra furthercomprises: computing for each point of the intermediate data sets in thefrequency-domain an ensemble base value based on the respective valuesof all intermediate data sets; subtracting the computed ensemble basevalues from the respective values of the intermediate data setsresulting in a corresponding base value centered spectrum for eachintermediate data set; and detecting signal intervals by extracting fromthe base value centered spectra for each frequency point variationsinduced by the system function and identifying frequency intervals withsignificant variation as signal intervals.
 2. The method of claim 1,wherein the variation parameter has Hertz as the unit of measurement andthe number of variation parameter values is in the range from 3 to 20.3. The method of claim 1, wherein the selected system function isselected from the group of: exponential decay function, exponentialslope function, or trigonometric function, multiplied with the NMRsignal data if the signal data is in the time-domain, and Lorentzfunction, Gaussian function, or Trapezium Function, used in aconvolution with the NMR signal data if the signal data is in thefrequency-domain.
 4. The method of claim 1, wherein detecting signalintervals comprises: generating a deviation spectrum from the pluralityof generated base value centered spectra; determining a noise value withthe noise signal corresponding to a normal distribution by computing themean value and the standard deviation for a non-signal interval in thedeviation spectrum, the non-signal interval corresponding to a frequencyinterval having a normal distribution; multiplying the computed standarddeviation with a predefined weighting factor wherein the weightingfactor determines a threshold probability for values in the deviationspectrum which are lower than or equal to the weighted noise value toqualify as noise values with said threshold probability; and determiningvalues in the deviation spectrum which are higher than the weightednoise value as NMR signal components.
 5. The method of claim 1, whereindetecting signal intervals comprises: generating an eigenspace matrixfrom the plurality of generated base value centered spectra; determiningthe absolute values of at least the first eigenvector of the matrix, theabsolute values representing the system function induced variations; andidentifying the absolute values which are greater than respectiveweighted noise values as NMR signal components.
 6. The method of claim1, wherein detecting signal intervals is performed by using iterativethresholding when extracting the variation for each frequency point fromthe base value centered spectra.
 7. The method of claim 1, furthercomprising: smoothening of the basis line in the original frequencyspectrum and performing a phase correction.
 8. The method of claim 1,wherein the provided variation parameter values are of the order of thehalf-width of signal peaks in the frequency-domain of the sampled NMRsignal data.
 9. A computer program product that when loaded into amemory of a computing device and executed by at least one processor ofthe computing device executes the steps of: receiving NMR signal dataproduced by a sample over time in response to an excitation pulse;selecting a predefined system function for application to the NMR signaldata for systematic variation of signal properties wherein the systemfunction has a different influence on NMR signal components than onnoise components of the sampled signal, the system function having avariation parameter to control the systematic variation; providing aplurality of variation parameter values wherein the selected parametervalues have differing values which are adapted for influencing, in thefrequency-domain, NMR signals with a signal intensity within a giveninterval around the mean signal intensity of the sampled signal, and NMRsignals with a signal width within a given interval around the meansignal width; generating for each variation parameter value acorresponding intermediate data set by applying the system function withthe respective variation parameter value to the NMR signal data;generating from each intermediate data set a respective base valuecentered spectrum in the frequency-domain wherein the respective basevalue centered spectra eliminate offsets from the intermediate data setsincluding approximating corresponding base values representing theactual offsets, wherein generating base value centered spectra furthercomprises: computing for each point of the intermediate data sets in thefrequency-domain an ensemble base value based on the respective valuesof all intermediate data sets; subtracting the computed ensemble basevalues from the respective values of the intermediate data setsresulting in a corresponding base value centered spectrum for eachintermediate data set; and detecting signal intervals by extracting fromthe base value centered spectra for each frequency point variationsinduced by the system function and identifying frequency intervals withsignificant variation as signal intervals.
 10. The computer programproduct of claim 9, wherein the variation parameter has Hertz as theunit of measurement and the number of variation parameter values is inthe range from 3 to
 20. 11. The computer program product of claim 9,wherein the selected system function is selected from the groupconsisting of: exponential decay function, exponential slope function,or trigonometric function, multiplied with the NMR signal data if thesignal data is in the time-domain, and Lorentz function, Gaussianfunction, and Trapezium Function, used in a convolution with the NMRsignal data if the signal data is in the frequency-domain.
 12. Thecomputer program product of claim 9, wherein detecting signal intervalscomprises: generating a deviation spectrum from the plurality ofgenerated base value centered spectra; determining a noise value withthe noise signal corresponding to a normal distribution by computing themean value and the standard deviation for a non-signal interval in thedeviation spectrum, the non-signal interval corresponding to a frequencyinterval having a normal distribution; multiplying the computed standarddeviation with a predefined weighting factor wherein the weightingfactor determines a threshold probability for values in the deviationspectrum which are lower than or equal to the weighted noise value toqualify as noise values with said threshold probability; and determiningvalues in the deviation spectrum which are higher than the weightednoise value as NMR signal components.
 13. The computer program productof claim 9, wherein detecting signal intervals comprises: generating aneigenspace matrix from the plurality of generated base value centeredspectra; determining the absolute values of at least the firsteigenvector of the matrix, the absolute values representing the systemfunction induced variations; and identifying the absolute values whichare greater than respective weighted noise values as NMR signalcomponents.
 14. The computer program product of claim 9, whereindetecting signal intervals is performed by using iterative thresholdingwhen extracting the variation for each frequency point from the basevalue centered spectra.
 15. The computer program product of claim 9,wherein the provided variation parameter values are of the order of thehalf-width of signal peaks in the frequency-domain of the sampled NMRsignal data.
 16. A computer system for improved NMR signal detection inNMR spectroscopy, the system, comprising: an interface module configuredto receive NMR signal data produced by a sample over time in response toan excitation pulse; an intermediate data set generator configured to:select a predefined system function for application to the NMR signaldata for systematic variation of signal properties wherein the systemfunction has a different influence on NMR signal components than onnoise components of the sampled signal, the system function having avariation parameter to control the systematic variation; provide aplurality of variation parameter values wherein the selected parametervalues have differing values which are adapted for influencing, in thefrequency-domain, NMR signals with a signal intensity within a giveninterval around the mean signal intensity of the sampled signal, and NMRsignals with a signal width within a given interval around the meansignal width; generate for each variation parameter value acorresponding intermediate data set by applying the system function withthe respective variation parameter value to the sampled NMR signal data;a base value centered spectrum generator configured to: generate fromeach intermediate data set, in the frequency-domain, a respective basevalue centered spectrum wherein the respective base value centeredspectra eliminate offsets from the intermediate data sets includingapproximating corresponding base values representing the actual offsets,by computing for each point of the intermediate data sets in thefrequency-domain an ensemble base value based on the respective valuesof all intermediate data sets, and subtracting the computed ensemblebase values from the respective values of the intermediate data setsresulting in a corresponding base value centered spectrum for eachintermediate data set; and a signal detector configured to: detectsignal intervals by extracting from the base value centered spectra foreach frequency point variations induced by the system function, and toidentify frequency intervals with significant variations as signalintervals.
 17. The system of claim 16, wherein the signal detector isconfigured to: generate a deviation spectrum from the plurality ofgenerated base value centered spectra; determine a noise value with thenoise signal corresponding to a normal distribution by computing themean value and the standard deviation for a non-signal interval in thedeviation spectrum, the non-signal interval corresponding to a frequencyinterval having a normal distribution; multiply the computed noise valuewith a predefined weighting factor wherein the weighting factordetermines a threshold probability for values in the deviation spectrumwhich are lower than or equal to the weighted noise value to qualify asnoise values with said threshold probability; and determine values inthe deviation spectrum which are higher than the weighted noise value asNMR signal components.
 18. The system of claim 16, wherein the signaldetector is configured to: generate an eigenspace matrix from theplurality of generated base value centered spectra; determine theabsolute values of the product of the first m eigenvectors andeigenvalues of the matrix, the absolute values representing the systemfunction induced variations; and identify the absolute values which aregreater than respective weighted noise values as NMR signal components.19. The system of claim 16, wherein the signal detector is configuredto: detect signal intervals by using iterative thresholding whenextracting the variation for each frequency point from the base valuecentered spectra.
 20. A NMR spectrometer comprising the system of claim16.